{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2932d625",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-15T03:00:46.150284Z",
     "start_time": "2024-05-15T03:00:46.019289Z"
    }
   },
   "outputs": [],
   "source": [
    "import gym\n",
    "import numpy as np\n",
    "from IPython import display\n",
    "import matplotlib\n",
    "from gym.envs.toy_text.frozen_lake import generate_random_map\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "49ed7a65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-15T03:00:46.404091Z",
     "start_time": "2024-05-15T03:00:46.397261Z"
    }
   },
   "outputs": [],
   "source": [
    "class GymHelper:\n",
    "    def __init__(self,env,figsize=(3,3)):\n",
    "        self.env=env\n",
    "        self.figsize=figsize\n",
    "        plt.figure(figsize=figsize)\n",
    "        self.img=plt.imshow(env.render())\n",
    "    def render(self,title=None):\n",
    "        img_data=self.env.render()\n",
    "        self.img.set_data(img_data)\n",
    "        display.display(plt.gcf())\n",
    "        display.clear_output(wait=True)\n",
    "        if title:\n",
    "            plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0815c29d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-15T03:00:46.832945Z",
     "start_time": "2024-05-15T03:00:46.828971Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from tqdm import *\n",
    "import collections\n",
    "import time\n",
    "import random\n",
    "import sys\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "48d712f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-15T03:00:47.857216Z",
     "start_time": "2024-05-15T03:00:47.403930Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(12)\n",
    "env=gym.make(\"FrozenLake-v1\",desc=generate_random_map(size=4),is_slippery=False,render_mode=\"rgb_array\")\n",
    "env.reset()\n",
    "gym_helper=GymHelper(env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9b7816f9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-15T03:03:54.856896Z",
     "start_time": "2024-05-15T03:03:54.850252Z"
    }
   },
   "outputs": [],
   "source": [
    "class Dyna_Q:\n",
    "    def __init__(self,env,eps=0.01,alpha=0.1,gamma=0.9,n_planning=[0,2,20]):\n",
    "        self.n_action=env.action_space.n\n",
    "        self.n_space=env.observation_space.n\n",
    "        self.Q_table=np.zeros([self.n_space,self.n_action])\n",
    "        self.alpha=alpha\n",
    "        self.gamma=gamma\n",
    "        self.eps=eps\n",
    "        self.n_planning=n_planning\n",
    "        self.model=dict()\n",
    "    def choose_action(self,state):\n",
    "        if np.random.random()<self.eps:\n",
    "            action=np.random.choice(self.n_action)\n",
    "        else:\n",
    "            action=np.argmax(self.Q_table[state])\n",
    "        return action\n",
    "    def q_learning(self,state,action,reward,next_state):\n",
    "        td_error=reward+self.gamma*self.Q_table[next_state].max()-self.Q_table[self.n_action]\n",
    "        self.Q_table[(state,action)]+=self.alpha*td_error\n",
    "    def update(self,state,action,reward,next_state):\n",
    "        self.q_learning(state,action,reward,next_state)\n",
    "        self.model[(state,action)]=reward,next_state\n",
    "        for n in range(self.n_planning):\n",
    "            (state,action),(reward,next_state)=random.choice(list(self.model.items()))\n",
    "            self.q_learning(state,action,reward,next_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d09024d4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-15T03:03:55.196951Z",
     "start_time": "2024-05-15T03:03:55.168760Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                         | 0/1500 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_5096\\1724253124.py\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[0mbuffer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnext_state\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m         \u001b[0meps_reward\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mreward\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m         \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnext_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;31m#         if len(agent.replay_buffer)>batch_size:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;31m#             agent.update(batch_size)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_5096\\763235387.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, state, action, reward, next_state)\u001b[0m\n\u001b[0;32m     19\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQ_table\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mtd_error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnext_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mq_learning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnext_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     22\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnext_state\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_planning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_5096\\763235387.py\u001b[0m in \u001b[0;36mq_learning\u001b[1;34m(self, state, action, reward, next_state)\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mq_learning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnext_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m         \u001b[0mtd_error\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreward\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgamma\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQ_table\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnext_state\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQ_table\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_action\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQ_table\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mtd_error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnext_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mq_learning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnext_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: setting an array element with a sequence."
     ]
    }
   ],
   "source": [
    "max_episodes=1500\n",
    "max_steps=1000\n",
    "\n",
    "#batch_size=32\n",
    "\n",
    "agent=Dyna_Q(env)\n",
    "eps_rewards=[]\n",
    "\n",
    "for episode in tqdm(range(max_episodes)):\n",
    "    state,_=env.reset()\n",
    "    eps_reward=0\n",
    "    buffer=[]\n",
    "    for step in range(max_steps):\n",
    "        action=agent.choose_action(state)\n",
    "        next_state,reward,terminated,truncated,info=env.step(action)\n",
    "        done=terminated or truncated\n",
    "        buffer.append((state,action,reward,next_state,done))\n",
    "        eps_reward+=reward\n",
    "        agent.update(state,action,reward,next_state)\n",
    "#         if len(agent.replay_buffer)>batch_size:\n",
    "#             agent.update(batch_size)\n",
    "        state=next_state\n",
    "        if done:\n",
    "            break\n",
    "\n",
    "    eps_rewards.append(eps_reward)\n",
    "    if episode % 40==0:\n",
    "        tqdm.write(\"Episode\"+str(episode)+\":\"+str(eps_reward))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f0f0475",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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